Method for selecting a clinical treatment plan tailored to patient defined health goals

ABSTRACT

The invention discloses a method by which the health care professional or patient may draw upon historical medical data concerning patients similarly situated in medical condition, to assist him/her in deciding on a clinical intervention procedure to select. This method is specifically tailored to the patient, as data is provided and evaluated from only similarly situated patients, and provides an expectation of potential outcome of the patient should one or the other of the options be selected. The invention further provides a database that may be used in order to provide this comparison based evaluation method. A computer based software system is further disclosed that implements the method. The invention more specifically provides a method by which a post-coronary event patient may make an informed decision of which post-coronary revascularization procedure to undergo in the future management of his disease. This method employs the patient&#39;s health status date (symptoms, function and quality of life), and provides projections of the patient&#39;s expected survival, risk, and 1-year health status outcome from the selection of revascularization procedure, such as Coronary Artery Bypass Grafting (CABG) or Percutaneous Coronary Intervention (PCI).

CROSS-REFERENCE TO RELATED APPLICATIONS

Any and all applications for which a foreign or domestic priority claim is identified in the Application Data Sheet as filed with the present application, are hereby incorporated by reference in their entirety under 37 CFR 1.57.

FIELD OF THE INVENTION

The present invention lies in the field of clinical treatment plan assessment and selection, as a method for selecting the particular regimen best suited for a particular patient's individual health goals and desired outcomes is disclosed. The invention also relates to the field of computer software programs, as the method for clinical treatment assessment and selection may be implemented through the use of a computer program and associated software program. The invention further relates to the field of cardiac patient care, as the described method of assessment and treatment selection is particularly defined in some aspects for use in the treatment of a patient that has cardiac disease, such as coronary artery disease.

BACKGROUND OF THE INVENTION Statement of the Problem

Patient-reported health status has been used as been used as an endpoint in clinical trials. Health status measures quantify the patient's perception of how a disease affects them. Specifically, these developed parameters have been created to allow the patient to report on a subjective level how their disease has affected their everyday function, disease or other symptoms, and generally their perceived quality of life. Apart from being reported as an endpoint, the information obtained from patients on these factors has not been examined or proposed for any other use.

Cardiovascular disease continues to be common in a large percentage of the population, despite improvements made in general in towards improvement in life-style to enhance heart heath. The patient with coronary artery disease, for example, after a cardiac event such as a heart attack, is and the heath-care professional are then faced with selecting a post-cardiac event treatment regimen or clinical intervention procedure, such as Coronary Artery Bypass grafting (CABG) or Percutaneous Coronary Intervention (PCI). Post-cardiac event treatment decisions are presented for the most part in a vacuum to the patient, as no statistical data or factually based decision tree criteria that can be of a full range of clinical outcomes including health status has to this been available against which the patient may make an informed and conscious decision

Current clinical evidence collected from patients having had one of two revascularization procedures have shown no difference in the patient survival data. However, survival data is only one criterion in examining and evaluating a selected revascularization procedure. Improving a patients' health status outcomes (symptoms, function, and quality of life) is an important critical goal of treatment selection, yet essentially no data about the health status outcomes after revascularization exists. With limited outcomes data to differentiate between the relative risks and benefits of CABG and PCI, for example, selecting a mode of coronary revascularization is currently determined almost exclusively by technical considerations and procedural risks. These factors in essence exclude patient participation in the decision making process.

A need continues to exist in the medical arts of a method that may be used that would allow both the patient and the attending health care professional to make a clinical health care treatment decision that would optimize the patient's desired heath goals and quality of life concerns. Such a method would incorporate the patient's individual age, sex, socioeconomic, demographic and clinical characteristics, and provide a patient-tailored set of options with the associated relative risk and success outcomes that are likely to be expected. Such a method does not currently exist in the art.

SUMMARY OF THE INVENTION

The invention in a general and overall sense provides a method for preparing a disease-specific database for use in assessing health care options available to a patient.

In some embodiments, the methods of the invention are employed in the evaluation and decision making process for treatment plan in post-cardiac event patients. Currently, no such method for the post-coronary event patient is described or available.

The database described as part of the present invention may in another aspect provide a method by which the post-coronary or other clinical patient, or his attending health care professional, may create a decision matrix that can be used to consider and select the most appropriate revascularization or other post-coronary event intervention procedure for the patient. This provides the patient with a set of options that is specifically tailored for that patient, and provides the patient with an assessment of the relative benefits and disadvantages associated with selecting one or another of the options being presented.

In another aspect, the invention provides a computer based software system that is devised so as to indicate relative risk associated with the selection of a particular revascularization protocol, given the specific health status of a given patient considering potential options for the treatment of their coronary disease.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A and 1B. FIG. 1A is a graph of angina frequency post-PCI (Percutaneous Coronary Intervention) (diamond is economically burdened patient; square is not economically burdened patient). Patients characterized as not economically burdened demonstrate a higher frequency of angina after PCI revascularization procedure. FIG. 1B is a graph of angina frequency in patients post-CABG revascularization. Patients characterized as economically burdened had about the same frequency of angina post CABG as did the patient population characterized as economically burdened.

FIGS. 2A and 2B—FIG. 2A is a graph demonstrating the frequency of angina in a post-coronary attack patient having had bypass surgery. The complications monitored in these patients were death (0.9%), stroke (1.5%), readmit (2%) and PTCA (less than 1%). FIG. 2B is a graph demonstrating the frequency of angina in a post-coronary event patient that had an angioplasty. The same complications were monitored in these patients, with a reported frequency of 0.1% death, 0.02% stroke, 30% readmission to the hospital, and 20% Re PTCA.

FIG. 3 block diagram illustrating a computer system according to one embodiment of the present invention.

FIG. 4 is a medical system according to one embodiment of the present invention.

FIG. 5 is a flow diagram illustrating one method of the medical system to the present invention.

FIG. 6 is another diagram illustrating one method of the present invention.

FIG. 7 is a flow diagram illustrating one step of the method of the present invention.

FIG. 8 is a flow diagram illustrating another step of the present invention.

FIG. 9 is a flow diagram illustrating another step of the present invention.

FIG. 10 is a flow diagram illustrating another step of the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

FIG. 3 shows computer system 300 configured adapted for use in a medical or clinical health care facility to identify an appropriate post-cardiac event regimen for an individual patient considering options for post-coronary event treatment, in accord with one embodiment of the invention. Computer system 300 may include processor 302, computer memory 304, and storage unit 306. In computer system 300, processor 302 is communicatively connected to computer memory 304 and to storage unit 306 for operating in accord with the invention. In one embodiment, computer system 300 is configured for identifying a disease state and demographics of the individual patient. Computer system 300 may assess health status parameters from the individual patient to provide a first data assessment profile and identify the projected health outcome desired by the individual patient based upon said individual preferences and goals. Computer system 300 may assess health status parameters from a population of patients having similar demographics to said individual patient, said population of patients having received different treatments, thereby providing a library of specific projected health outcomes for each different treatment. Upon assessing the health status parameters from the population of patients, Computer system 300 may select preferred outcomes from the library of specific projected health outcomes that similarly coincide with preferences and goals of the individual patient, present the preferred outcomes to the patient, and select a clinical treatment for the patient based on the preferred outcomes. In one embodiment of the invention, software 303 is configured for operatively controlling computer system 300 and may initially reside in storage unit 306. Upon initializing computer system 300, software 303 may be loaded in computer memory 304. Processor 302 may then software run 303.

FIG. 4 shows medical system 400 configured for configured identifying an appropriate post-cardiac event regimen for an individual patient considering options for post-coronary event treatment, in accord with one embodiment of the invention. Medical system 400 may include processor 404, storage unit 406, and interface 408. In medical system 400, storage unit 406 is configured for configured for storing group data in a database. The group data may comprise responses to a questionnaire having a plurality of questions regarding quality of life and demographic information. The response may be derived from a plurality of patients having survived a coronary event. A first group of the patients have received a post-coronary event revascularization procedure. A second group of the patients had not received the post-coronary event revascularization procedure. Demographics of the first and second groups of patients may be similar to those of the individual patient. In medical system 400, interface 408 is configured for receiving responses to the questions from the individual patient. In medical system 400, processor 404 is communicatively connected to interface 408 and to storage unit 306 for performing statistical analysis on the responses from the plurality of patients and from the individual patient. A comparison of the statistical analysis of the responses from the group of patients and from the individual patient may provide a basis upon which the individual patient may select a post-cardiac event treatment appropriate to preferences and goals of the individual patient.

With further regard to FIGS. 3 and 4, those skilled in the art should appreciate that storage unit 406 and storage unit 306 may illustratively represent the same storage memory and/or one or a combination of storage unit 306 and computer memory 304 within computer system 300. Processor 302 may incorporate functionality including processor 404, for example.

FIG. 5 shows a flow chart illustrating operation 500 of medical system 400, in accord with one method of the invention. Operation 500 commences in step 502. Processor 404 identifies a disease state and demographics of the individual patient, in step 504. Processor 404 assesses health status parameters of the individual patient, in step 506. Processor 404 identifies the projected health outcome desired by the individual patient based upon said individual preferences and goals, in step 508. Processor 404 assesses health status parameters from a population of patients having similar demographics to the individual patient to provide a library of specific projected health outcomes for each different treatment, in step 510. Processor 404 selects preferred outcomes from the library of specific projected health outcomes that similarly coincide with preferences and goals of the individual patient, in step 512. Interface 408 presents the preferred outcomes to the patient, in step 514. Processor 404 selects a clinical treatment for the patient based on the preferred outcomes, in step 516. Operation 500 ends in step 518.

Instructions that perform the operation discussed in FIG. 5 may be stored in storage media or computer memory. The instructions may be retrieved and executed by processor 404. Some examples of instructions include software, program code, and firmware. Some examples of storage media include memory devices, tapes, disks, integrated circuits, and servers. The instructions are operational when executed by processor 404 to direct processor 404 to operate in accord with the invention. Those skilled in the art are familiar with instructions and storage media.

FIG. 6 shows a flow chart illustrating operation 600 of medical system 400, in accord with one method of the invention. Operation 500 commences in step 502. Processor 404 identifies a disease state and demographics of the individual patient, in step 504. The demographics may include age, sex, economic burden, living situation, social support, employment status, type of employment, and education level of the individual patient. Processor 404 assesses set of health status parameters from the patient to provide a first data assessment profile, in step 605. Processor 404 assesses set of health status parameters from a first population of patients to provide a first reference data assessment profile, in step 610. Processor 404 assesses set of health status parameters from a second population of patients to provide a second reference data assessment profile, in step 612. Processor 404 assesses set of health status parameters from a third population of patients to provide a third reference data assessment profile, in step 608. The first, second, and third populations may have similar demographics as the individual patient and differing treatments and/or revascularization procedures. Processor 404 projects the survival and quality of life probability of the individual patient from the first, second, and third reference data assessment profiles to respectively provide first, second, and third projected post-procedural outcomes of the revascularization procedures and/or treatments, in steps 614, 616, and 618. The revascularization procedures may include a coronary artery bypass grafting (CABG) procedure and a Percutaneous Coronary Intervention (PCI) procedure. The treatment may include anti-coronary disease medication, diet modification, herbal remedy, and other non-surgical intervention procedure. Processor 404 compares the first projected post-procedural outcome to the second projected post-procedural outcome, in step 620. Processor 404 selects an appropriate revascularization procedure for the individual patient in response to the step of comparing, in step 622. Operation 600 ends in step 624.

FIG. 7 shows a flow chart illustrating step 605 of operation 600, in accord with one method of the invention. Step 605 enters through entry point 701. Processor 404 assess the individual patient's data, in step 402. Step 605 exits through exit point 703.

FIG. 8 shows a flow chart illustrating step 610 of operation 600, in accord with one method of the invention. Step 610 enters through entry point 801. Processor 404 may collect the first data assessment profile of the first group of patients, in step 802. Processor 404 may select the CABG revascularization procedure, in step 804. Step 610 exits through exit point 803.

FIG. 9 shows a flow chart illustrating step 612 of operation 600, in accord with one method of the invention. Step 612 enters through entry point 901. Processor 404 may collect the second data assessment profile of the second group of patients, in step 902. Processor 404 may select the PCI revascularization procedure, in step 904. Step 612 exits through exit point 903.

FIG. 10 shows a flow chart illustrating step 620 of operation 600, in accord with one method of the invention. Step 620 enters through entry point 1001. Processor 404 may perform a statistical analysis on the individual patient quality of life profile, in step 1002. The statistical analysis may indicate to the individual patient an appropriate post-cardiac clinical regimen. Processor 404 may compute a statistical average that indicates the appropriate post-cardiac clinical regimen based on the statistical analysis, in step 1004. Processor 404 may compute a statistical deviation about the average, in step 1006. Step 620 exits through exit point 1003.

Those skilled in the art should appreciate that operation 500 and 600 are shown for illustrative purposes and that certain changes or step sequences, such as those found in steps 504, 506, and 508, may be altered as a matter of design choice.

Example 1

The present example demonstrates the utility of the present invention for assembly and stratifying patient data as part of a tool that can be used by a patient or attending health care professional in making a choice of clinical treatment to pursue. In a general sense, the invention creates a patient outcome management system.

Using a collected, consecutive cohort of patients undergoing coronary revascularization, the critical patient and procedural characteristics that predict health status outcomes after a cardiac revascularization procedure, such as Coronary Artery Bypass Grafting (CABG) or Percutaneous Coronary Intervention (PCT), can be identified. The method of the present invention may be used in conjunction with any group of patients and collected disease-burdened population of people, such as a population of patients that suffer from arthritis, chronic obstructive pulmonary disease, cancer (brain, prostate, breast, skin, etc.), any type of peripheral vascular disease, by way of historical data may be collected and stratified according to a defined demographic profile of an individual patient considering his/her options for treatment of a disease or condition.

This demographically sorted data may then be screened to isolate those treatment regimens that resulted historically in the spectrum of health status outcomes and goals/priorities identified to be most important to the patient. From the treatment regimens that had provided at least the majority of results/consequences important to the patient, the patient may be presented with a focused set of health care treatment options to consider in making his/her decision.

By using the types of data outlined below, the patient and procedural characteristics that most influence a patients' peri-procedural and 1-year outcomes will be determined.

TABLE 1 Patient Data Clinical Data Demographics Comorbidities Economic burden Depression (MOS-D) Living Situation Rx setting - i.e. AMI Social Support Cardiac risk factors Employment Smoking Status Education SF-12/EuroQOL

For a patient having had a coronary disease, such as a heart attack, that is considering a revascularization procedure, the following specific data will also be tabulated and PCI (1639 patients), the present investigation identified specific post-coronary event outcomes that were identifiable with a specific selection of one revascularization event over another. The outcome data that was characteristic of the patients that were statistically analyzed for this system had been tallied one-year after the particular revascularization procedure was performed on them.

A substudy of some of these patients has been completed on 495 patients. Of these 224 patients received CABG revascularization procedure and 271 patients received a PCI revascularization procedure. All of these patients were administered baseline and monthly follow-up assessments for 6 months to model the recovery of health status after revascularization. Preliminary analysis of this substudy revealed identifiable trends in health status outcome that were linked to the treatment protocol elected.

Previous clinical trials had reported no survival differences between PCI and CABG. Presently published results however, clearly showed that after PCI, patients are more dependent on anti-anginal mediations than CABG. Recently published clinical trial data demonstrates that 21.1% of PCI patients as compared with 41.5% of CABG patients were free of anti-anginal medications 1 year after treatment (P<0.001). Given the greater need for medications after PCI, the present investigation considers the variable that patient may have difficulty in affording their healthcare, and that these patients may have difficulty in affording their healthcare, and that these patients may have a worse health status aft PCI as compared with CABG.

Example 2 Angina Frequency

The present example demonstrates the utility of the invention for considering health status rather than survival for managing the health care options to be presented to a patient. The present example also illustrates the utility of a new set of risk-stratification variables that are important in the medical decision variables that are important in the medical decision making process. The present example also demonstrates the utility of the present example as an efficient mechanism of collecting data about patient's current health status and of new risk-stratification variables that are useful in projecting anticipated outcomes.

The present invention further presents the inclusion of interdual patient data that is being accumulated through each new decision making event, back into the pool of data or population data that may be used/is used in a subsequent pool of patients. In this manner, the population database is constantly being updated, as well as opportunities for new treatment regimens becoming part of the decision-making process system.

The need to integrate multiple sources of data and to depict multiple types of outcomes has led to the present inventors' development of yet another aspect of the invention, a decision making tool PREDICT™, that is to be used in tailoring treatment choices to individual patients.

At the time of revascularization, the 34.3% of patients reporting an economic burden had significantly more frequent angina than those who did not (SAQ Angina Frequency score (range=0-100 where higher scores indicate less angina)=60+−26 vs. 69+−25 for CABG; 52+−30 vs. 67+−25 for PCI (p<0.01 for both)).

During the 6 months of follow-up, however, a persistent disparity in angina control was noted after PCI (Repeated Measures ANOVA controlling for all baseline differences between groups: F=6.6, p=0.009) but not after CABG (F=0.06, p=0.8). Similar findings were noted for SAQ physical function and quality of life domains as well. The mechanism by which economically disadvantaged patients are unable to attain the same health status after PCI as economically secure patients is unclear. The absence of such a disparity in CABG may indicate that patients who have difficulty affording medical care might preferentially select surgical revascularization.

Prophetic Example 3 Identification of Determinants of Health Status

The key determinants of health status (symptoms, function, quality of life) after PCI and CABG through robust analyses of an existing database. Using the types of data described in the Table above, we will determine the key predictor variables for angina frequency, physical limitation and quality of life as measured by 12-month, post-procedure SAQ scores will be tabulated and statistically analyzed.

TABLE 2 Procedural Data Outcome Data Number of diseased vessels Seattle Angina Questionnaire Percent Stenosis Short Form-12 Ejection Fraction EuroQOL Technique of revasc variation Hospitalizations Treatment success Repeat procedures Complications Survival

Data reduction will be done with clustering, stepwise variable selection and factor analysis techniques to identify the most parsimonious set of data that needs to be collected. Internal (bootstrap) validation and comparisons with external data sources will be used to validate selected variables. Given the anticipated error in predicting outcomes with any statistical model (due to unmeasured patient variability and the role of chance) the patient will not be presented with a single projected outcome for each SAQ domain. Rather, these data will be used to stratify patients and then generate the range of observed outcomes seen in similar patients treated with both PCI and CABG (see example below). This will make concrete the range of previously observed outcomes (among similar patients) and allow patients and their physicians to choose a treatment strategy that has the best trade-off between projected distributions of outcome and risk (the latter coming from the models of STS, ACC, Emory, NY State, Northern New England, etc.).

While the format and elements of outcomes projections will change, an example of the types of data that we envision presenting is shown below. In this example, a 72 year old women with diabetes, normal LV function, and difficulty affording her healthcare can see the trade-offs between the better symptom distribution, greater peri-operative risk and lower likelihood of repeat admissions and revascularization procedures of bypass surgery as compared with PCI. Such presentations of outcomes data will allow patients (and their physicians) to be more involved and, ultimately, satisfied with the process of selecting a revascularization strategy.

Prophetic Example 4 Computer Program Using Observational Data Bases for Revascularization Decision Making Processes

Observational databases will be used to facilitate treatment decisions for patients considering revascularization. The PREDICT™ instrument provides the vehicle that will be employed to accomplish this task. Four distinct components create PREDICT™. First, an interface for data collection is required. Second, a software program takes collected created so that collected data may be transformed into clinically meaningful distributions of projected outcomes. Third, a mechanism for customizing PREDICT™'s output so that patient-valued and readily interpretable outcomes may be displayed. And finally, the infrastructure for tracking outcomes of patients using PREDICT™ must be created so that the system can continue to grow as new treatment technologies are introduced.

PREDICT™'s networked software architecture optimization algorithms. To assist in keeping this project appraised of the most recent developments in the rapidly evolving field of software and computing design.

The first step in designing PREDICT™ is to create a mechanism for collecting the data elements identified in Example 1. The present decision tool will be seamlessly integrated into the flow of patient care. This is particularly important in the setting of coronary revascularization where the decision to perform revascularization may be made with the first injection of contrast during diagnostic angiography. The ultimate design of data collection will depend on the number and types of data needed, the current plan is to write a Palm Pilot® application for the collection of critical data elements, to identify the optimal point in patient care for their acquisition, and to synchronize collected data to a server so that we can generate the needed outcomes reports.

The second step in developing PREDICT™ is to build a software application for generating the observed outcomes distributions and predictions. Given the need to link multiple potential data sources and to incorporate these data into a series of models for output generation, a distributed network-based software system will be developed using an extensible information system (XML) to represent critical data elements. The open architecture, scalability and cross-platform utility of XML make it ideal for creating PREDICT™. Our approach will involve creating and implementing the architectural specifications for a flexible, scalable system to include data collection and representation, compute engine development, and mechanisms for generating customizable output.

The third step in creating PREDICT™ is to make the output readily interpretable. Current evidence suggests that patient's better recall and understand facts presented in numeric, probabilistic terms. In fact, the lack of access to numeric estimates has been shown to encourage patients to overestimate treatment benefits and to underestimate risk. Furthermore, the use of numeric summaries of expected outcomes improves the accuracy of physician-patient communication. In addition to the format of data, the frame of the message, in terms of health benefits (gains) or costs (losses) is also important. Research on message framing has produced mixed results with some health behaviors being influenced more by loss-oriented messages (e.g., breast self-exams) and others by emphasizing health gains (e.g. smoking cessation). Alternative ways of framing numeric, probabilistic outcomes presentations and then conduct a series of focus groups with different data formulations (e.g., negatively vs. positively framed, different visual formats) to finalize the present approach will be developed. Insights from these exercises will provide invaluable feedback in perfecting PREDICT™'s output. Ultimately, a customizable menu of choices will be created for patients and physicians to select those outcomes that are most relevant to them. This will allow one patient, who is most concerned about returning to work, to select that outcome whereas another may choose angina relief or quality of life as the outcome that most concerns them. This will maximize the likelihood that feedback will address the goals and values of each individual patient.

The final step in creating PREDICT™ is to build an infrastructure for follow-up. As patient data is entered into PREDICT™, it can provide the baseline assessment for following that individual's outcome over time. For this reason, we will design PREDICT™'s software to allow maximal use of all collected data. Once accomplished, collected baseline data will be synchronized with procedural and follow-up databases so that 1 year after initial treatment, patients can be contacted for follow-up. As follow-up data is captured, those patients' data will enter the repository from which future patients will see the distribution of outcomes associated with patients who were similar to them. This creates a continuously evolving system that minimizes the delay in updating outcomes projections in an era of rapid technological change.

Prophetic Example 5 Pilot Test of Predict™

Throughout the process of developing PREDICT™, ongoing feedback from patients and physicians will be acquired through individual interviews and focus groups. Ultimately, however, an explicit demonstration of its feasibility will be needed. The final goal of this proposal will be to conduct a 1-month pilot test. The complete design of such testing cannot be definitively described because the precise parameters that will dictate usage will evolve from the steps outlined in Specific Aims 1 and 2. Conceptually, however, we plan to adopt the following basic approach: PREDICT™ will be implemented among a consecutive cohort of patients and describe the time required for implementation, the percent of patients eligible in whom the tool was used, the physicians' assessment whether the tool provides value to outweigh the time required in its use and patients' satisfaction with decision making as assessed by the Satisfaction with Decision scale.⁹ Once the insights from this pilot study are reviewed and analyzed, a multicenter trial of PREDICT™ will be created to assess its impact on patient choices for revascularization, satisfaction, cost and outcome. 

1. (canceled)
 2. A non-transitory computer readable storage medium storing instructions that when executed by a computer system, cause the computer system to perform operations comprising: retrieving from non-transitory memory clinical information regarding a first individual patient; assessing health status parameters of the first individual patient; identifying, from clinical information for a plurality of patients stored in a database, other patients having similar health status parameters as the first individual patient, the health status parameters comprising disease states and demographics; identifying health outcomes associated with the identified other patients and corresponding treatments received by the identified other patients, wherein a first set of the identified other patients received a first treatment and a second set of the identified other patients received a second treatment; identifying projected health outcomes for the first individual patient based at least in part on health outcomes of the first set of the identified other patients that received the first treatment and on health outcomes of the second set of the identified other patients that received the second treatment; generating a presentation of the identified projected health outcomes and enabling a selection of a treatment corresponding to one of the identified projected health outcomes.
 3. The non-transitory computer readable storage medium as defined in claim 2, the operations further comprising: projecting a survival probability or a quality of life probability for the first individual patient; predicting health status outcomes for at least one individual patient suffering from at least one of a condition of: coronary artery disease, or peripheral vascular disease, or congestive heart failure, or chronic obstructive pulmonary disease, or cancer, by isolating treatment regimens for at least one of the conditions.
 4. The non-transitory computer readable storage medium as defined in claim 2, wherein the clinical information for the plurality of patients includes economic burden for at least one patient.
 5. The non-transitory computer readable storage medium as defined in claim 2, wherein the clinical information for the plurality of patients includes economic burden and health status assessment for at least one patient.
 6. The non-transitory computer readable storage medium as defined in claim 2, wherein the first individual patient's demographics include living situation, and the identified projected health outcomes are identified based at least in part on the first individual patient's living situation.
 7. The non-transitory computer readable storage medium as defined in claim 2, wherein the first individual patient's demographics include social support and, the identified projected health outcomes are identified based at least in part on the first individual patient's social support.
 8. The non-transitory computer readable storage medium as defined in claim 2, wherein the first individual patient's demographics include employment status, and the identified projected health outcomes are identified based at least in part on the first individual patient's employment status.
 9. The non-transitory computer readable storage medium as defined in claim 2, wherein the first individual patient's demographics include type of employment, and the identified projected health outcomes are identified based at least in part on the first individual patient's type of employment.
 10. The non-transitory computer readable storage medium as defined in claim 2, wherein the first individual patient's demographics include education level, and the identified projected health outcomes are identified based at least in part on the first individual patient's education level.
 11. The non-transitory computer readable storage medium as defined in claim 2, wherein identifying projected health outcomes for the first individual patient is based in part on a preference of the first individual patient and on a goal of the first individual patient.
 12. The non-transitory computer readable storage medium as defined in claim 2, the operations further comprising projecting a survival probability or a quality of life probability for the first individual patient.
 13. The non-transitory computer readable storage medium as defined in claim 2, the operations further comprising indicating an appropriate post-cardiac clinical regime using a statistical analysis on a quality of life profile associated with the first individual patient.
 14. The non-transitory computer readable storage medium as defined in claim 2, the operations further comprising indicating an appropriate post-cardiac clinical regime by performing a statistical analysis on the first individual patient's quality of life profile, computing a statistical average based on the statistical analysis, and computing a statistical deviation about the average.
 15. The non-transitory computer readable storage medium as defined in claim 2, the operations further comprising: predicting health status outcomes for at least one individual patient suffering from one or more of the following conditions: arthritis, chronic obstructive pulmonary disease, cancer, or a peripheral vascular disease, by isolating treatment regimens for the one or more conditions that resulted historically in health status outcomes and goals corresponding to those of the first individual patient to thereby provide the first individual patient with a corresponding set of health care options from which to select.
 16. A computer implemented method comprising: retrieving from non-transitory memory clinical information regarding a first individual patient; identifying, from clinical information for a plurality of patients stored in a database, other patients having similar health status parameters as the first individual patient, the health status parameters comprising disease states and demographics; identifying, by the computer system, health outcomes associated with the identified other patients and corresponding treatments received by the identified other patients, wherein a first set of the identified other patients received a first treatment and a second set of the identified other patients received a second treatment; identifying, by the computer system, projected health outcomes for the first individual patient based at least in part on health outcomes of the first set of the identified other patients that received the first treatment and on health outcomes of the second set of the identified other patients that received the second treatment; generating, by the computer system, a presentation of the identified projected health outcomes and enabling a selection of a treatment corresponding to one of the identified projected health outcomes.
 17. The method as defined in claim 16, the method further comprising: projecting, by the computer system, a survival probability or a quality of life probability for the first individual patient; predicting, by the computer system, health status outcomes for at least one individual patient suffering from at least one of a condition of: coronary artery disease, or peripheral vascular disease, or congestive heart failure, or chronic obstructive pulmonary disease, or cancer, by isolating treatment regimens for at least one of the conditions.
 18. The method as defined in claim 16, wherein the first individual patient's demographics include education level, and the identified projected health outcomes are identified based at least in part on the first individual patient's education level.
 19. The method as defined in claim 16, wherein identifying projected health outcomes or the first individual patient is based in part on a preference of the first individual patient and on a goal of the first individual patient.
 20. The method as defined in claim 16, the method further comprising further comprising projecting a survival probability or a quality of life probability for the first individual patient.
 21. The method as defined in claim 16, the method further comprising indicating an appropriate post-cardiac clinical regime using a statistical analysis on a quality of life profile associated with the first individual patient.
 22. The method as defined in claim 16, the method further comprising indicating an appropriate post-cardiac clinical regime by performing a statistical analysis on the first individual patient's quality of life profile, computing a statistical average based on the statistical analysis, and computing a statistical deviation about the average.
 23. The method as defined in claim 16, the method further comprising: predicting health status outcomes for at least one individual patient suffering from one or more of the following conditions: arthritis, chronic obstructive pulmonary disease, cancer, or a peripheral vascular disease, by isolating treatment regimens for the one or more conditions that resulted historically in health status outcomes and goals corresponding to those of the first individual patient to thereby provide the first individual patient with a corresponding set of health care options from which to select.
 24. A computer system comprising: a processing device; non-transitory computer readable storage medium storing instructions that when executed by the processing device, cause the computer system to perform operations comprising: retrieving from non-transitory memory clinical information regarding a first individual patient; assessing health status parameters of the first individual patient; identifying, from clinical information for a plurality of patients stored in a database, other patients having similar health status parameters as the first individual patient, the health status parameters comprising disease states and demographics; identifying health outcomes associated with the identified other patients and corresponding treatments received by the identified other patients, wherein a first set of the identified other patients received a first treatment and a second set of the identified other patients received a second treatment; identifying projected health outcomes for the first individual patient based at least in part on health outcomes of the first set of the identified other patients that received the first treatment and on health outcomes of the second set of the identified other patients that received the second treatment; generating a presentation of the identified projected health outcomes and enabling a selection of a treatment corresponding to one of the identified projected health outcomes.
 25. The system as defined in claim 24, the operations further comprising: projecting a survival probability or a quality of life probability for the first individual patient; predicting health status outcomes for at least one individual patient suffering from at least one of a condition of: coronary artery disease, or peripheral vascular disease, or congestive heart failure, or chronic obstructive pulmonary disease, or cancer, by isolating treatment regimens for at least one of the conditions.
 26. The system as defined in claim 24, wherein the first individual patient's demographics include education level, and the identified projected health outcomes are identified based at least in part on the first individual patient's education level.
 27. The system as defined in claim 24, wherein identifying projected health outcomes for the first individual patient is based in part on a preference of the first individual patient and on a goal of the first individual patient.
 28. The system as defined in claim 24, the operations further comprising further comprising projecting a survival probability or a quality of life probability for the first individual patient.
 29. The system as defined in claim 24, the operations further comprising indicating an appropriate post-cardiac clinical regime using a statistical analysis on a quality of life profile associated with the first individual patient.
 30. The system as defined in claim 24, the operations further comprising indicating an appropriate post-cardiac clinical regime by performing a statistical analysis on the first individual patient's quality of life profile, computing a statistical average based on the statistical analysis, and computing a statistical deviation about the average.
 31. The system as defined in claim 24, the operations, the operations further comprising: predicting health status outcomes for at least one individual patient suffering from one or more of the following conditions: arthritis, chronic obstructive pulmonary disease, cancer, or a peripheral vascular disease, by isolating treatment regimens for the one or more conditions that resulted historically in health status outcomes and goals corresponding to those of the first individual patient to thereby provide the first individual patient with a corresponding set of health care options from which to select. 